基于贝叶斯策略网络的软行动者批判学习

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qin Yang, Ramviyas Parasuraman
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引用次数: 0

摘要

策略是指代理为实现目标而选择可用行动的规则。对于一个在危险、非结构化和动态环境中工作、资源有限的智能代理来说,采用合理的策略具有挑战性,但对于提高系统效用、降低总体成本和提高任务成功概率至关重要。本文提出了一种基于贝叶斯链的新型分层策略分解方法,可将复杂的策略分解为多个简单的子策略,并将它们之间的关系组织为贝叶斯策略网络(BSN)。我们将这种方法集成到最先进的 DRL 方法--软行为者批判(SAC)中,并通过将多个子策略组织为一个联合策略,建立了相应的贝叶斯软行为者批判(BSAC)模型。我们的方法在 OpenAI Gym 环境中的标准连续控制基准上取得了最先进的性能。结果表明,BSAC 方法潜力巨大,能显著提高训练效率。此外,我们还将话题延伸到多代理系统(MAS),讨论了潜在的研究领域和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Strategy Networks Based Soft Actor-Critic Learning

A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. Our method achieves the state-of-the-art performance on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. Furthermore, we extend the topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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